Amino acid encoding schemes for machine learning methods

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

Volumn , Issue , 2011, Pages 327-333

  Zamani, Masood ^a   Kremer, Stefan C ^a  

^a UNIVERSITY OF GUELPH   (Canada)

Author keywords

amino acids; artificial neural networks; machine learning; substitution matrix

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BIOLOGICAL SPECIMENS; COMPUTATIONAL BIOLOGY; ENCODING SCHEMES; ENCODINGS; MACHINE LEARNING METHODS; MACHINE LEARNING TECHNIQUES; MACHINE-LEARNING; NUMERICAL REPRESENTATION; PHYSICOCHEMICAL PROPERTY; PROTEIN SEQUENCES; PROTEIN SYNTHESIS; SEQUENCE ALIGNMENTS; SUBSTITUTION MATRIX; SYMBOLIC DATA; VECTOR REPRESENTATIONS;

AMINO ACIDS; BIOINFORMATICS; BIOSYNTHESIS; LEARNING SYSTEMS; MATRIX ALGEBRA; NEURAL NETWORKS;

ENCODING (SYMBOLS);

EID: 84855990488     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBMW.2011.6112394     Document Type: Conference Paper

References (18)

1. R. Swanson, "A vector representation for amino acid sequences,"Bulletin of mathematical biology, vol. 46, no. 4, pp. 623-639, 1984.
2. Blomap: an encoding of amino acids which improves signal peptide cleavage site prediction. Citeseer, 2005.
3. K. Zimmermann and J. Gibrat, "Amino acid" little big bang": Representing amino acid substitution matrices as dot products of euclidian vectors," BMC bioinformatics, vol. 11, no. 1, p. 4, 2010.
4. N. Qian and T. Sejnowski, "Predicting the secondary structure of globular proteins using neural network models," Journal of Molecular Biology, vol. 202, no. 4, pp. 865-884, 1988.
5. B. Rost and C. Sander, "Prediction of protein secondary structure at better than 70% accuracy," Journal of Molecular Biology, vol. 232, pp. 584-584, 1993.
6. G. Pollastri, D. Przybylski, B. Rost, and P. Baldi, "Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles," Proteins Structure Function and Genetics, vol. 47, no. 2, pp. 228-235, 2002. (Pubitemid 34263335)
7. M. Dayhoff, R. Schwartz, and B. Orcutt, "A model of evolutionary change in proteins," Atlas of Protein Sequence and Structure, vol. 5, no. Suppl 3, pp. 345-352, 1978.
8. S. Henikoff and J. Henikoff, "Amino acid substitution matrices from protein blocks," Proceedings of the National Academy of Sciences of the United States of America, vol. 89, no. 22, pp. 10 915-10 919, 1992.
9. S. Haykin, Neural networks and learning machines. Prentice Hall Upper Saddle River, NJ, 2009, vol. 10.
10. D. Rumelhart, G. Hinton, and R. Williams, "Learning internal representations by error propagation. parallel distributed processing, vol. 1," Foundations. MIT Press, Cambridge, 1986.
11. P. Baldi and S. Brunak, "Bioinformatics: The machine learning approach." 1998.
12. H. Hu, Y. Pan, R. Harrison, and P. Tai, "Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier,"NanoBioscience, IEEE Transactions on, vol. 3, no. 4, pp. 265-271, 2004.
13. H. Lac and S. Kremer, "Inducing fold dynamics from known protein structures using machine learning," Ph.D. dissertation, CIS, University of Guelph, April 2009.
14. W. Taylor, "The classification of amino acid conservation*, "Journal of theoretical Biology, vol. 119, no. 2, pp. 205-218, 1986.
15. C. Wu and J. McLarty, Neural networks and genome informatics. Elsevier Science, 2000, vol. 1.
16. M. Zvelebil, G. Barton, W. Taylor, and M. Sternberg, "Prediction of protein secondary structure and active sites using the alignment of homologous sequences," Journal of Molecular Biology, vol. 195, no. 4, pp. 957-961, 1987.
17. S. Kawashima and M. Kanehisa, "AAindex: amino acid index database," Nucleic acids research, vol. 28, no. 1, p. 374, 2000. (Pubitemid 30047811)
18. P. Chou and G. Fasman, "Prediction of the secondary structure of proteins from their amino acid sequence." Advances in enzymology and related areas of molecular biology, vol. 47, p. 45, 1978.